Filipino nurse migrants in Western Canada: An oral history
Bibliographic record
Abstract
The Philippines is often identified as the largest producer of nurses for export globally and Filipino nurses are joining the Canadian health care workforce at a steady rate contributing to the growing diversity of nursing practitioners. To date, little historical analysis exists of their experiences. This study explores Filipino nurses’ biographical oral histories in order to provide insight into their immigrant journey and integration into the Western Canadian health care workforce. The study examines how their individual experiences are intertwined with larger migration patterns, educational trends, and foreign presence in the Philippines. The study focuses in particular on Filipino nurse immigration to British Columbia and Alberta, using oral history interviews with immigrant Filipino nurses in these two provinces and selected nurses still in the Philippines. Through exploration of life and work experiences, the study illustrates larger social trends reflected in individuals’ stories, as well as illustrating how larger social pressures were experienced by individuals. The study contributes an important historical understanding of the significant impact Filipino nurse immigration has on the Canadian health care system in the broader context of colonization and nurse migration. Complementary to the impact on the Canadian system, how the educational, economic, and societal context of Filipino nurses in the Philippines shapes this phenomenon will be highlighted. The paper concludes that life history of a relatively small number of Filipino nurses provides important insight in larger migration dynamics and workforce tensions in nursing and health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".